Private and public networks, such as the Internet, continue to grow at an exponential rate. These rapidly expanding networks consume a tremendous amount of power, which is typically supplied from traditional electrical power grids. As such, most networked computers are either tethered to electrical wall outlets or require periodic charging at electrical wall outlets. Many networked computers, however, have benefitted tremendously by being self-powered. For example, emergency communication systems, computing devices in rural areas where access to traditional power sources is limited, etc.
However, current self-powered devices, such as devices that obtain energy from solar power, typically waste much of their power communicating with other networked computing devices. As such, it would be highly desirable to provide a self-powered device (SPD) that consumes less power when communicating with other devices in a network.
Wireless sensor networks have been proposed for a wide range of monitoring applications in various industries, such as health care, energy, transportation, infrastructure, agriculture, security, the environment and many other fields. Billions of active sensors have been installed that can wirelessly connect to networks and capture data. Such an installed base of sensors creates issues such as powering such sensors as well as transporting and storing the data received from the sensors.
Various conventional approaches have been developed in response to particular constraints. Microelectronics companies are concerned with creating low power devices with energy harvesting technologies and improved batteries. Communications companies are continuing to build networks with more radio towers, improved data compression and adherence to interference regulations. Cloud computing companies are developing approaches for storing more data with data structures suited for the expected exponential growth in capturing data. Such conventional approaches are unlikely to be sufficient with the grand vision of the “Internet of Things”. The communications bandwidth (and data storage on the Internet to implement such an approach) could include hundreds of times as many sensors as conventional smartphones, potentially consuming the highest amount of energy of the Internet.
Most conventional approaches are based on autonomous sensor notes that are always on and capture and transmit large amounts of sensor data to a network that always needs to be listening and recording the transmissions. The network then needs to relay back an acknowledgment to the sensor that the data was accurately received. If scaled to a trillion sensors, a huge number of devices would need to be implemented with batteries that would be unaffordable (or at least inconvenient) to change. A huge amount of traffic on already congested networks would further increase the power needs of each device, since higher antenna output would likely be needed to overcome interference and noise.
Different approaches are needed. The motivation of a better approach stems from a more fundamental question—Why do we need all of this data anyway? The data and related power needs of capturing, transmitting and recording data are highly context and application dependent. Most conventional sensors blindly capture and transmit data. Such sensors assume the application needs or knows what to do with that data. Some data inevitably are more important than others. The importance of data can vary with time and with the knowledge embedded in the application.
It would be desirable to implement a predictive power management in a wireless sensor network.